Network reciprocity by coexisting learning and teaching strategies

Jun Tanimoto, Markus Brede, Atsuo Yamauchi

Research output: Contribution to journalArticle

64 Citations (Scopus)

Abstract

We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

Original languageEnglish
Article number032101
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume85
Issue number3
DOIs
Publication statusPublished - Mar 21 2012

Fingerprint

Reciprocity
learning
education
Pairwise Comparisons
Strategy
Learning
Teaching
Simulation
simulation
Model

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Network reciprocity by coexisting learning and teaching strategies. / Tanimoto, Jun; Brede, Markus; Yamauchi, Atsuo.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 85, No. 3, 032101, 21.03.2012.

Research output: Contribution to journalArticle

@article{8699705b41f5450f92976c96979c3321,
title = "Network reciprocity by coexisting learning and teaching strategies",
abstract = "We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.",
author = "Jun Tanimoto and Markus Brede and Atsuo Yamauchi",
year = "2012",
month = "3",
day = "21",
doi = "10.1103/PhysRevE.85.032101",
language = "English",
volume = "85",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "3",

}

TY - JOUR

T1 - Network reciprocity by coexisting learning and teaching strategies

AU - Tanimoto, Jun

AU - Brede, Markus

AU - Yamauchi, Atsuo

PY - 2012/3/21

Y1 - 2012/3/21

N2 - We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

AB - We propose a network reciprocity model in which an agent probabilistically adopts learning or teaching strategies. In the learning adaptation mechanism, an agent may copy a neighbor's strategy through Fermi pairwise comparison. The teaching adaptation mechanism involves an agent imposing its strategy on a neighbor. Our simulations reveal that the reciprocity is significantly affected by the frequency with which learning and teaching agents coexist in a network and by the structure of the network itself.

UR - http://www.scopus.com/inward/record.url?scp=84859012422&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859012422&partnerID=8YFLogxK

U2 - 10.1103/PhysRevE.85.032101

DO - 10.1103/PhysRevE.85.032101

M3 - Article

AN - SCOPUS:84859012422

VL - 85

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 3

M1 - 032101

ER -